Nahla M H Elsaid1, Nadine L Dispenza2, R Todd Constable1,3, Hemant D Tagare1,2, and Gigi Galiana1
1Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States, 2Department of Biomedical Engineering, Yale University, New Haven, CT, United States, 3Neurosurgery, Yale University, New Haven, CT, United States
1Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States, 2Department of Biomedical Engineering, Yale University, New Haven, CT, United States, 3Neurosurgery, Yale University, New Haven, CT, United States
This work presents quantitative T2-maps
and a full T2w-image series generated from an ordinary single contrast T2w-dataset
using the growing Constrained Alternating Minimization for Parameter mapping (g-CAMP)
reconstruction method.
Figure 1. A framework of g-CAMP algorithm with four
mps, where the color of the k-space-lines represents acquisition at a
different
∆TE.
g-CAMP applies the CAMP algorithm to the center bands first (cycle 1) until it converges. Then g-CAMP grows the number of mps to include band 'a' (cycle 2) and finally, it includes all the bands (cycle 3).
Figure 2. Simulation results showing the ground truth
T2 maps versus the maps reconstructed using g-CAMP, with the
difference images and the masked absolute differences (showing errors inside the
brain). In addition, regression analyses are plotted between pixel values of the g-CAMP T2
maps versus those of the ground truth.